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Implementation of Real Data for Financial Market Simulation Using Clustering, Deep Learning, and Artificial Financial Market

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PRIMA 2020: Principles and Practice of Multi-Agent Systems (PRIMA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12568))

Abstract

In this paper, we propose a new scheme for implementing the machine-learned trader-agent model in financial market simulations based on real data. The implementation is only focused on the high-frequency-trader market-making (HFT-MM) strategy. We first extract order data of HFT-MM traders from the real order data by clustering. Then, using the data, we build a deep learning model. Using the model, we build an HFT-MM trader model for simulations. In the simulations, we compared our new model and a traditional HFT-MM trader model in terms of divergence of the ordering behaviors. Our new trader model outperforms the traditional model. Moreover, we also found an obstacle of combination of data and simulation.

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Notes

  1. 1.

    Usually, due to the limitation of transactions per one VS, traders use multiple VS.

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Acknowledgement

We thank the Japan Exchange Group, Inc. for providing the data. This research was supported by MEXT via Exploratory Challenges on Post-K computer (study on multilayered multiscale space-time simulations for social and economic phenomena). This research also used the computational resources of the HPCI system provided by the Information Technology Center at The University of Tokyo, and the Joint Center for Advanced High Performance Computing (JCAHPC) through the HPCI System Research Project (Project ID: hp190150).

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Authors and Affiliations

  1. School of Engineering, The University of Tokyo, Tokyo, Japan

    Masanori Hirano, Kiyoshi Izumi & Hiroki Sakaji

  2. Center for Data Science Education and Research, Shiga University, Shiga, Japan

    Hiroyasu Matsushima

Authors
  1. Masanori Hirano
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  2. Hiroyasu Matsushima
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  3. Kiyoshi Izumi
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  4. Hiroki Sakaji
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Corresponding author

Correspondence to Masanori Hirano .

Editor information

Editors and Affiliations

  1. Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  2. University of Tasmania, Tasmania, TAS, Australia

    Quan Bai

  3. University of Alcalá, Alcala de Henares, Spain

    Iván Marsá Maestre

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Hirano, M., Matsushima, H., Izumi, K., Sakaji, H. (2021). Implementation of Real Data for Financial Market Simulation Using Clustering, Deep Learning, and Artificial Financial Market. In: Uchiya, T., Bai, Q., Marsá Maestre, I. (eds) PRIMA 2020: Principles and Practice of Multi-Agent Systems. PRIMA 2020. Lecture Notes in Computer Science(), vol 12568. Springer, Cham. https://doi.org/10.1007/978-3-030-69322-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-69322-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69321-3

  • Online ISBN: 978-3-030-69322-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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